Airborne Platform for Ice-Accretion and Coatings Tests with Ultrasonic Readings (PICTUR)
Bibliographic record
Abstract
Hazardous atmospheric icing conditions occur at sub-zero temperatures when droplets come into contact with aircraft and freeze, degrading aircraft performance and handling, introducing bias into some of the vital measurements needed for aircraft operation (e.g., air speed). Nonetheless, government regulations allow certified aircraft to fly in limited icing environments. The capability of aircraft sensors to identify all hazardous icing environments is limited. To address the current challenges in aircraft icing detection and protection, we present herein a platform designed for in-flight testing of ice protection solutions and icing detection technologies. The recently developed Platform for Ice-accretion and Coatings Tests with Ultrasonic Readings (PICTUR) was evaluated using CFD simulations and installed on the National Research Council Canada (NRC) Convair-580 aircraft that has flown in icing conditions over North East USA, during February 2022. This aircraft is a flying laboratory, equipped with more than 40 sensors providing a comprehensive characterization of the flight environment including measurements of temperature, pressure, wind speed and direction, water droplet size and number distribution, and hydrometeor habits imagery. The flight tests of the platform included assessment of passive icephobic coatings as well as heat-assisted tests. Monitoring tools included visual high resolution, real-time inspection of the surface as well as detection of surface ice using NRC’s Ultrasonic Ice Accretion Sensors (UIAS). In this paper, we present the new platform and show some preliminary commissioning results of PICTUR, collected inflight under, predominantly, supercooled small droplets and supercooled large drops (SLD) icing conditions. The combination of the platform and the complementary sensors on the aircraft demonstrated an effective and unique technique for icing studies in a natural environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".